Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "204" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 33 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 33 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460016 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.367088 | 14.528194 | 1.504378 | -0.933803 | -0.067668 | 0.002393 | 15.186500 | 2.032486 | 0.5844 | 0.5997 | 0.3517 | nan | nan |
| 2460015 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.562701 | 15.054163 | 1.591091 | -0.907284 | -0.126621 | -0.051198 | 19.911177 | 1.465677 | 0.5943 | 0.6077 | 0.3490 | nan | nan |
| 2460014 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.493350 | 16.209914 | 1.002552 | -0.953606 | 0.667515 | 1.933933 | 13.568719 | 2.908702 | 0.5672 | 0.5904 | 0.3579 | nan | nan |
| 2460013 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.810690 | 15.100832 | 1.645372 | -0.901238 | -0.019929 | -0.144283 | 23.522584 | 1.817934 | 0.5885 | 0.6084 | 0.3565 | nan | nan |
| 2460012 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.184732 | 14.218853 | 1.525768 | -1.044245 | 0.209154 | 0.225999 | 31.606949 | 2.915612 | 0.5918 | 0.6119 | 0.3503 | nan | nan |
| 2460011 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.533821 | 14.982535 | 1.871330 | -1.406044 | 0.318607 | 0.492208 | 23.478389 | 1.919856 | 0.6178 | 0.6341 | 0.3453 | nan | nan |
| 2460010 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.652974 | 16.408093 | 1.380507 | -0.952993 | 0.250468 | 0.504899 | 20.579433 | 1.722007 | 0.6290 | 0.6528 | 0.3499 | nan | nan |
| 2460009 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.229927 | 15.777658 | 1.920736 | -1.044024 | -0.578002 | 0.031408 | 23.962913 | 2.055676 | 0.6289 | 0.6519 | 0.3531 | nan | nan |
| 2460008 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 11.381250 | 17.989565 | 1.893594 | -1.235403 | -0.229223 | 0.029073 | 7.913632 | -0.885049 | 0.6644 | 0.6818 | 0.3188 | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.782420 | 13.975582 | 1.463748 | -0.974904 | 0.446794 | 0.706373 | 20.501409 | 1.516528 | 0.6380 | 0.6568 | 0.3411 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5947 | 0.6265 | 0.3427 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.866459 | 13.470266 | 1.377859 | -0.817637 | -0.520635 | 0.558461 | 14.176720 | 1.077141 | 0.6101 | 0.6396 | 0.3800 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.507131 | 14.423999 | 1.461406 | -0.785468 | -0.228583 | 0.370365 | 28.990270 | 1.764292 | 0.6154 | 0.6467 | 0.3863 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.488666 | 15.662447 | 2.318113 | -0.842786 | 0.184163 | 0.068674 | 15.903692 | 1.706884 | 0.6292 | 0.6566 | 0.3955 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.145420 | 15.786604 | 1.598493 | -1.081327 | -0.047883 | 0.451697 | 12.314952 | 1.322992 | 0.6221 | 0.6530 | 0.3862 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.949441 | 15.831723 | 1.337040 | -0.909357 | -0.345002 | 1.112052 | 13.731795 | 1.222212 | 0.6198 | 0.6484 | 0.3776 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.173877 | 17.125055 | 1.048396 | -0.924982 | -0.054238 | 0.824020 | 10.173869 | 1.680888 | 0.5993 | 0.6419 | 0.3962 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.863209 | 18.088739 | 1.160198 | -0.918574 | -0.330339 | 0.859614 | 11.055742 | 1.435963 | 0.6191 | 0.6410 | 0.3882 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.418205 | 15.011195 | 1.109612 | -0.906967 | -0.263759 | 0.508114 | 11.480546 | 1.346782 | 0.6185 | 0.6437 | 0.3855 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.899121 | 15.468054 | 0.992833 | -0.705727 | -0.552008 | 0.581681 | 8.164161 | 0.582492 | 0.6179 | 0.6465 | 0.3858 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.371366 | 18.468430 | 1.109736 | -0.995610 | -0.624666 | 0.423567 | 8.925919 | 1.197870 | 0.6207 | 0.6489 | 0.3762 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.683172 | 15.348108 | 1.236350 | -0.943067 | -0.433297 | 0.816038 | 17.483123 | 2.747577 | 0.6216 | 0.6475 | 0.3771 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.330808 | 18.436099 | 1.249414 | -1.019471 | -0.271568 | 0.921522 | 7.903177 | -0.417586 | 0.6451 | 0.6727 | 0.3350 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.452478 | 17.477661 | 1.277231 | -0.986402 | -0.524777 | 0.491682 | 22.843202 | 1.962370 | 0.6242 | 0.6511 | 0.3812 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.195449 | 15.918147 | 1.451734 | -0.955345 | -0.094204 | 0.267634 | 14.464194 | 0.800021 | 0.6372 | 0.6660 | 0.3594 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 11.940781 | 15.761856 | 1.179431 | -0.957230 | -0.191051 | 1.070948 | 12.078833 | -0.125436 | 0.6577 | 0.6821 | 0.3212 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.988475 | 13.749906 | 1.011972 | -0.950885 | 0.075113 | 0.679339 | 2.075295 | -1.088488 | 0.7139 | 0.7135 | 0.2807 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.866387 | 14.430121 | 1.137850 | -1.066805 | 6.910587 | 7.503192 | 13.595887 | 1.646375 | 0.6350 | 0.6539 | 0.3792 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.745407 | 13.693480 | 6.318278 | 4.411032 | 1.890925 | 0.172134 | 5.761267 | 4.844407 | 0.6271 | 0.6658 | 0.3061 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 15.186500 | 14.528194 | 10.367088 | -0.933803 | 1.504378 | 0.002393 | -0.067668 | 2.032486 | 15.186500 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 19.911177 | 15.054163 | 10.562701 | -0.907284 | 1.591091 | -0.051198 | -0.126621 | 1.465677 | 19.911177 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 16.209914 | 10.493350 | 16.209914 | 1.002552 | -0.953606 | 0.667515 | 1.933933 | 13.568719 | 2.908702 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 23.522584 | 10.810690 | 15.100832 | 1.645372 | -0.901238 | -0.019929 | -0.144283 | 23.522584 | 1.817934 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 31.606949 | 10.184732 | 14.218853 | 1.525768 | -1.044245 | 0.209154 | 0.225999 | 31.606949 | 2.915612 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 23.478389 | 10.533821 | 14.982535 | 1.871330 | -1.406044 | 0.318607 | 0.492208 | 23.478389 | 1.919856 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 20.579433 | 10.652974 | 16.408093 | 1.380507 | -0.952993 | 0.250468 | 0.504899 | 20.579433 | 1.722007 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 23.962913 | 10.229927 | 15.777658 | 1.920736 | -1.044024 | -0.578002 | 0.031408 | 23.962913 | 2.055676 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 17.989565 | 17.989565 | 11.381250 | -1.235403 | 1.893594 | 0.029073 | -0.229223 | -0.885049 | 7.913632 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 20.501409 | 9.782420 | 13.975582 | 1.463748 | -0.974904 | 0.446794 | 0.706373 | 20.501409 | 1.516528 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 14.176720 | 7.866459 | 13.470266 | 1.377859 | -0.817637 | -0.520635 | 0.558461 | 14.176720 | 1.077141 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 28.990270 | 8.507131 | 14.423999 | 1.461406 | -0.785468 | -0.228583 | 0.370365 | 28.990270 | 1.764292 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 15.903692 | 9.488666 | 15.662447 | 2.318113 | -0.842786 | 0.184163 | 0.068674 | 15.903692 | 1.706884 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 15.786604 | 9.145420 | 15.786604 | 1.598493 | -1.081327 | -0.047883 | 0.451697 | 12.314952 | 1.322992 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 15.831723 | 8.949441 | 15.831723 | 1.337040 | -0.909357 | -0.345002 | 1.112052 | 13.731795 | 1.222212 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 17.125055 | 9.173877 | 17.125055 | 1.048396 | -0.924982 | -0.054238 | 0.824020 | 10.173869 | 1.680888 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 18.088739 | 9.863209 | 18.088739 | 1.160198 | -0.918574 | -0.330339 | 0.859614 | 11.055742 | 1.435963 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 15.011195 | 15.011195 | 8.418205 | -0.906967 | 1.109612 | 0.508114 | -0.263759 | 1.346782 | 11.480546 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 15.468054 | 15.468054 | 8.899121 | -0.705727 | 0.992833 | 0.581681 | -0.552008 | 0.582492 | 8.164161 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 18.468430 | 18.468430 | 10.371366 | -0.995610 | 1.109736 | 0.423567 | -0.624666 | 1.197870 | 8.925919 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 17.483123 | 8.683172 | 15.348108 | 1.236350 | -0.943067 | -0.433297 | 0.816038 | 17.483123 | 2.747577 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 18.436099 | 18.436099 | 10.330808 | -1.019471 | 1.249414 | 0.921522 | -0.271568 | -0.417586 | 7.903177 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | ee Temporal Discontinuties | 22.843202 | 17.477661 | 9.452478 | -0.986402 | 1.277231 | 0.491682 | -0.524777 | 1.962370 | 22.843202 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 15.918147 | 8.195449 | 15.918147 | 1.451734 | -0.955345 | -0.094204 | 0.267634 | 14.464194 | 0.800021 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 15.761856 | 11.940781 | 15.761856 | 1.179431 | -0.957230 | -0.191051 | 1.070948 | 12.078833 | -0.125436 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 13.749906 | 10.988475 | 13.749906 | 1.011972 | -0.950885 | 0.075113 | 0.679339 | 2.075295 | -1.088488 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 14.430121 | 14.430121 | 10.866387 | -1.066805 | 1.137850 | 7.503192 | 6.910587 | 1.646375 | 13.595887 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 204 | N19 | RF_maintenance | nn Shape | 13.693480 | 13.693480 | 8.745407 | 4.411032 | 6.318278 | 0.172134 | 1.890925 | 4.844407 | 5.761267 |